Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network
Emergency management agencies must address the challenges presented by frequent flooding events. Remote sensing imagery provides a means for timely monitoring of rapidly changing water bodies during flooding events; but manual analysis of remote sensing (RS) images however, is labor intensive and ti...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322200320X |
| _version_ | 1797950828739297280 |
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| author | Bofei Zhao Haigang Sui Junyi Liu |
| author_facet | Bofei Zhao Haigang Sui Junyi Liu |
| author_sort | Bofei Zhao |
| collection | DOAJ |
| description | Emergency management agencies must address the challenges presented by frequent flooding events. Remote sensing imagery provides a means for timely monitoring of rapidly changing water bodies during flooding events; but manual analysis of remote sensing (RS) images however, is labor intensive and time consuming. Automated methods are effective, but the post-classification comparison method for flood inundation detection is subject to error accumulation, and the direct change detection method is limited by the accuracy of flood mapping and the difficulty of obtaining training samples. To overcome these challenges, a flood inundation detection network (Siam-DWENet) that achieves high-accuracy inundation detection is proposed. In Siam-DWENet, an innovative cross-task transfer learning strategy incorporates an attention mechanism and multi-scale pyramid structure based on Siamese architectures. This approach realizes a priori knowledge transfer-based flood inundation detection with a limited number of training samples. Comparative experiments on Siam-DWENet and other methods using two flooding SAR datasets to evaluate the accuracy of flood detection. The experimental results indicate that Siam-DWENet outperforms other change detection methods and makes the inundation area edge more accurate when dealing with complex backgrounds, achieving an average OA of 0.887 and F1 of 0.865 in flood inundation detection tasks. |
| first_indexed | 2024-04-10T22:21:18Z |
| format | Article |
| id | doaj.art-c2306cbd663d4270b845ccbb4f1b0ee6 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-10T22:21:18Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-c2306cbd663d4270b845ccbb4f1b0ee62023-01-18T04:30:00ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-02-01116103132Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese networkBofei Zhao0Haigang Sui1Junyi Liu2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaCorresponding author.; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaEmergency management agencies must address the challenges presented by frequent flooding events. Remote sensing imagery provides a means for timely monitoring of rapidly changing water bodies during flooding events; but manual analysis of remote sensing (RS) images however, is labor intensive and time consuming. Automated methods are effective, but the post-classification comparison method for flood inundation detection is subject to error accumulation, and the direct change detection method is limited by the accuracy of flood mapping and the difficulty of obtaining training samples. To overcome these challenges, a flood inundation detection network (Siam-DWENet) that achieves high-accuracy inundation detection is proposed. In Siam-DWENet, an innovative cross-task transfer learning strategy incorporates an attention mechanism and multi-scale pyramid structure based on Siamese architectures. This approach realizes a priori knowledge transfer-based flood inundation detection with a limited number of training samples. Comparative experiments on Siam-DWENet and other methods using two flooding SAR datasets to evaluate the accuracy of flood detection. The experimental results indicate that Siam-DWENet outperforms other change detection methods and makes the inundation area edge more accurate when dealing with complex backgrounds, achieving an average OA of 0.887 and F1 of 0.865 in flood inundation detection tasks.http://www.sciencedirect.com/science/article/pii/S156984322200320XDeep learningSiam-DWENetFlood inundation detectionTransfer learningSAR |
| spellingShingle | Bofei Zhao Haigang Sui Junyi Liu Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network International Journal of Applied Earth Observations and Geoinformation Deep learning Siam-DWENet Flood inundation detection Transfer learning SAR |
| title | Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network |
| title_full | Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network |
| title_fullStr | Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network |
| title_full_unstemmed | Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network |
| title_short | Siam-DWENet: Flood inundation detection for SAR imagery using a cross-task transfer siamese network |
| title_sort | siam dwenet flood inundation detection for sar imagery using a cross task transfer siamese network |
| topic | Deep learning Siam-DWENet Flood inundation detection Transfer learning SAR |
| url | http://www.sciencedirect.com/science/article/pii/S156984322200320X |
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